Clinical trial · Observational
Predicting Pathological Complete Response in Rectal Cancer Using Machine Learning
Development and Validation of a Machine Learning Model Based on Clinical and MRI Features for Predicting Pathological Complete Response in Rectal Cancer Following Neoadjuvant Chemoradiotherapy
NCT07509632CI-TRIAL-00107228active not recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study aims to develop and validate a robust machine learning-based prediction model utilizing baseline clinical data and magnetic resonance imaging (MRI) features. The objective is to preoperatively predict the probability of achieving a pathological complete response (pCR) in patients with locally advanced rectal cancer (CRC) following neoadjuvant chemoradiotherapy (nCRT).
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Machine Learning | — | UNRESOLVED | — |
| Neoadjuvant Chemoradiotherapy | — | UNRESOLVED | — |
| Pathological Complete Response | — | UNRESOLVED | — |
| Rectal Cancers | Malignant Rectal Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| No interventions | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG)
- timeFrame
- Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery).
- description
- The primary endpoint is the occurrence of pCR, assessed by two independent pathologists using the AJCC/CAP Tumor Regression Grade (TRG) system. TRG 0 (no viable cancer cells, only fibrosis or mucin pools) is defined as a positive outcome (pCR). TRG 1 to 3 are combined and defined as a negative outcome (non-pCR). The predictive performance of the model will be evaluated utilizing several metrics including the Area Under the ROC Curve (AUC), Precision-Recall (PR) curve, Calibration curve, and Decision Curve Analysis (DCA).
Secondary outcomes (5)
- measure
- Area under the receiver operating characteristic curve (AUC) of the prediction model
- timeFrame
- At the completion of model development and validation
- description
- To evaluate the discrimination performance of the model for pCR prediction
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. Patients with histopathologically confirmed rectal adenocarcinoma; 2. Clinical stage cT3-4, or cN+, or M1 advanced rectal cancer; 3. Received standardized neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy; 4. Underwent total mesorectal excision (TME) after the completion of neoadjuvant therapy, with complete postoperative pathological data available. Exclusion Criteria: 1. Previous history of other malignant tumors; 2. Incomplete clinical data; 3. Underwent emergency surgery during nCRT; 4. Complicated with systemic infection or hematological diseases.
References
Publications (2)
- BACKGROUNDKong JC, Guerra GR, Warrier SK, Lynch AC, Michael M, Ngan SY, Phillips W, Ramsay G, Heriot AG. Prognostic value of tumour regression grade in locally advanced rectal cancer: a systematic review and meta-analysis. Colorectal Dis. 2018 Jul;20(7):574-585. doi: 10.1111/codi.14106. Epub 2018 May 8. PMID 29582537
- BACKGROUNDMaas M, Nelemans PJ, Valentini V, Das P, Rodel C, Kuo LJ, Calvo FA, Garcia-Aguilar J, Glynne-Jones R, Haustermans K, Mohiuddin M, Pucciarelli S, Small W Jr, Suarez J, Theodoropoulos G, Biondo S, Beets-Tan RG, Beets GL. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010 Sep;11(9):835-44. doi: 10.1016/S1470-2045(10)70172-8. Epub 2010 Aug 6. PMID 20692872